import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
%matplotlib inline
%matplotlib inline
# Read all the Calibration images
image_paths = glob.glob("./camera_cal/calibration*.jpg")
## Object Point and Image Point array creation for each image
objpoints = [] # Object Points Array
imgpoints = [] # Image Points Array
pass_img = []
#Identify 6*9 object points
objp = np.zeros((6*9,3),np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Read each image , find chessboard coreners and draw the chess board corners
# Store paths of images in which coreners were found.
for img_path in image_paths:
# Read Image from the path
img = cv2.imread(img_path)
# Convert image to gray scale.
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find Chess board coreners in the image
ret , corners = cv2.findChessboardCorners(gray,(6,9),None)
# If chess board coreners were found store the points in list
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
img = cv2.drawChessboardCorners(img,(9,6),corners,ret)
pass_img.append(img_path)
# Function to calibrate camera
# Accepts image , object points and image points
# Return Undistored image
def cal_undistort(img, objpoints, imgpoints):
# Convert image to gray scale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Perform Camera Calibration
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
# Undistore the images.
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
# Undistort all the Camera Calibration Images.
cnt =0
f, ax = plt.subplots(len(pass_img), 2,figsize=(10,50))
for img_path in pass_img:
# Read in an image
img = cv2.imread(img_path)
# Undistor the image
undistorted = cal_undistort(img, objpoints, imgpoints)
# Show original and undistored images.
ax[cnt,0].imshow(img)
ax[cnt,0].set_title('Original Image', fontsize=15)
ax[cnt,1].imshow(undistorted)
ax[cnt,1].set_title('Undistorted Image', fontsize=15)
cnt+=1
mpimg.imsave('./output_images/undistorted_images/calibration'+str(cnt)+'.jpg',undistorted)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
# Funtion to perform thresholding
# Color image is formed by using combinded binary of s, h and sobelx
def threshold(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
img = np.copy(img)
# Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
h_channel = hls[:,:,0]
l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
# Convert to Gray Scale
gray_img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
# Sobel x
sobelx = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# S channel Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
# H channel Threshold color channel
h_binary = np.zeros_like(h_channel)
h_binary[(h_channel >= sx_thresh[0]) & (h_channel <= sx_thresh[1])] = 1
# Form a combinded image
combinded_binary = np.zeros_like(sxbinary)
combinded_binary[((h_binary==1)&(s_binary==1))|(sxbinary==1)]=1
# Form a color binary by using combinded binary
color_binary = np.dstack((combinded_binary,combinded_binary,combinded_binary))*255
return color_binary
# Test Image
image = mpimg.imread("./test_images/test5.jpg")
result = threshold(image)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(result,'gray')
ax2.set_title('Thresholding Result', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
# Store the Thresholded Image
mpimg.imsave('./output_images/threshold_images/Test5.jpg',result)
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = []
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
self.count =0
# Function to update the Lane Lines X , Y and Polynomial Coefficients
def update_coordinates(self,x,y,fit):
# Save X and Y values
self.allx = x
self.ally = y
# Average Filter
self.count +=1
if self.count>20:
self.count = 20
# remove the oldest entry to out_buf if the length of out_buf is more than 'outbuf_len'
self.current_fit.pop(0)
self.recent_xfitted.pop(0)
# Add new values
self.current_fit.append(fit)
self.recent_xfitted.append(x)
# Average the values
self.bestx = np.average(self.recent_xfitted,axis=0)
self.best_fit = np.average(self.current_fit,axis=0)
right_lane_obj = Line()
left_lane_obj = Line()
# Function to perform Perspective Transform
# Accepts Image to be transformed and Source Points
# Returns warped image and Inverse M for reversing the transform in future
def perspective_transform(img,src):
# Store the image size in an array
img_size = (img.shape[1],img.shape[0])
# Identify the destination points
dst = np.float32([[(350, 720), (350, 0), (980, 0), (980, 720)]])
# Use CV2 funtion to perform perspective transform
M = cv2.getPerspectiveTransform(src, dst)
# Use CV2 funtion to Inverse Perspective transform for future use
Minv = cv2.getPerspectiveTransform(dst, src)
# Use CV2 funtion and transform the image
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
# Return Warped and Inverse Perspective Transform Matrix for future use.
return warped, Minv
# Function to find lanes
# Accepts binary warped image
# Returns Image with overlayed lanes , polynomial coefficients, x and y values of left, right lanes
def find_lanes(binary_warped,left_fit,right_fit):
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Load the best fit values
left_fit = left_lane_obj.best_fit
right_fit = right_lane_obj.best_fit
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
if((left_fit != None) and (right_fit != None)):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
else:
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[np.int32(binary_warped.shape[0]/2):,:], axis=0)
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),
(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),
(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Highlight Left Lane in Red and Right in Blue
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Create Evenly Spaced Y values and extract x values for each x
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Limit the X to be with in the image boundaries
left_fitx[(left_fitx>1280)]=1279
right_fitx[(right_fitx>1280)]=1279
left_fitx[left_fitx<0]=0
right_fitx[right_fitx<0]=0
# Highlight the Left and Right Lanes in Green
out_img[np.int32(ploty),np.int32(left_fitx)] = [255,255,0]
out_img[np.int32(ploty),np.int32(right_fitx)] = [255,255,0]
# Return Image with overlayed lanes , polynomial coefficients, x and y values of left, right lanes
return out_img,np.dstack((left_fit,right_fit)),np.dstack((ploty,left_fitx,right_fitx))
# Function to draw lanes and fill the space between lanes in green
# Input: Warped Image , Original Undistored image , Left & Right X , Inverse Perspective Matrix
# Returns: Image with overlayed lanes and area between lanes filled in green
def draw_lines(warped,undist,ploty,left_fitx,right_fitx,Minv):
margin = 15
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Draw the lane onto the warped blank image
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Highlight Left Lane in Red and Right in Blue
cv2.fillPoly(color_warp, np.int_([left_line_pts]), (255,0, 0))
cv2.fillPoly(color_warp, np.int_([right_line_pts]), (0,0,255))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
return(result)
# Function to calculate Radius of Curvature at the vehicle edge
# Input: Y Points , Left and Right Lane X values
# Output: Left and Right Lange Curvature
def cal_radius_curvature(ploty,leftx,rightx):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Evaluate the radius at the edge of the vehicle
y_eval = np.max(ploty)
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
return left_curverad,right_curverad
# Source Points for Perspecitive Transformation
points = np.float32([[(200, 720), (570, 470), (720, 470), (1130, 720)]])
# window settings
window_width = 50
window_height = 80 # Break image into 9 vertical layers since image height is 720
margin = 100 # How much to slide left and right for searching
# Read all the Test image path
test_image_paths = glob.glob("./test_images/*.jpg")
f, ax = plt.subplots(len(test_image_paths), 2,figsize=(20,60))
left_fit =[]
right_fit=[]
cnt =0
for imagep in test_image_paths:
# Empty the Polynomials as these images are different
left_fit =[]
right_fit=[]
# Read the Image from the path
image = mpimg.imread(imagep)
# Undistort the image
undistorted = cal_undistort(image, objpoints, imgpoints)
mpimg.imsave('./output_images/undistorted_images/test'+str(cnt)+'.jpg',undistorted)
# Peform Thresholding
thr_image = threshold(undistorted)
mpimg.imsave('./output_images/threshold_images/test'+str(cnt)+'.jpg',thr_image)
# Perspecitive Transform the Images
warped,persp_invM = perspective_transform(thr_image,points)
mpimg.imsave('./output_images/perspective_images/test'+str(cnt)+'.jpg',warped)
# Convert to gray scale
warped_gray = cv2.cvtColor(warped,cv2.COLOR_BGR2GRAY)
# Find Lane Lines in the images
out_img,poly_fit,coord_fit = find_lanes(warped_gray,left_fit,right_fit)
# Draw the lane lines
left_fit = poly_fit[0,:,0]
right_fit = poly_fit[0,:,1]
out_img = draw_lines(warped_gray,undistorted,coord_fit[0,:,0],coord_fit[0,:,1],coord_fit[0,:,2],persp_invM)
####### Calculate the Radius of curvature #####
left_rad_cuv,right_rad_cuv = cal_radius_curvature(coord_fit[0,:,0],coord_fit[0,:,1],coord_fit[0,:,2])
# Average the Radius of curvature over 60 samples
radius = ((left_rad_cuv+right_rad_cuv)/2)
# Print the radius of curvature
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(out_img,'Radius of Curvature = '+str(radius)+'(m)',(100,30), font, 1, (255,255,255), 2, cv2.LINE_AA)
# Calculte the offest
offset = ((coord_fit[0,:,1][(image.shape[0]-1)]+coord_fit[0,:,2][(image.shape[0]-1)]-image.shape[1])*(3.7/1400))
font = cv2.FONT_HERSHEY_SIMPLEX
# Print the offset
cv2.putText(out_img,'Vehicle is '+str(offset)+'(m) left of center',(100,80), font, 1, (255,255,255), 2, cv2.LINE_AA)
mpimg.imsave('./output_images/output/test'+str(cnt)+'.jpg',out_img)
# Plot the result
f.tight_layout()
ax[cnt,0].imshow(image)
ax[cnt,0].set_title('Original Image', fontsize=20)
ax[cnt,1].imshow(out_img)
ax[cnt,1].set_title('Overlayed Image', fontsize=15)
cnt+=1
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
# Pipeline to process the images
# Input: Raw Image from Video
# Return: Processed Image with overlayed Lane Lines
def process_image(image):
# Clear Pervious Image Curve values and Radius of curvature.
left_fit =[]
right_fit =[]
radius = []
rad_count =0
#### PipeLine Start #####
# Undistort the Image
undistorted = cal_undistort(image, objpoints, imgpoints)
# Perform Thresholding
image_gray = threshold(undistorted)
# Perspective Transform the image
warped, persp_invM = perspective_transform(image_gray,points)
# Convert to Gray scale
warped_gray = cv2.cvtColor(warped,cv2.COLOR_BGR2GRAY)
# Find Lane Lines
out_img,poly_fit,coord_fit = find_lanes(warped_gray,left_fit,right_fit)
# Store Left and Right Polynomial
left_fit = poly_fit[0,:,0]
right_fit = poly_fit[0,:,1]
# Update the Left and Right Object Parameters
left_lane_obj.update_coordinates(coord_fit[0,:,1],coord_fit[0,:,0],(left_fit))
right_lane_obj.update_coordinates(coord_fit[0,:,2],coord_fit[0,:,0],(right_fit))
# Draw the Lane Lines on the image
out_img = draw_lines(warped_gray,undistorted,coord_fit[0,:,0],coord_fit[0,:,1],coord_fit[0,:,2],
persp_invM)
####### Calculate the Radius of curvature #####
left_lane_obj.radius_of_curvature,right_lane_obj.radius_of_curvature = cal_radius_curvature(coord_fit[0,:,0],left_lane_obj.bestx,right_lane_obj.bestx)
# Average the Radius of curvature over 60 samples
radius.append((left_lane_obj.radius_of_curvature+right_lane_obj.radius_of_curvature)/2)
rad_count+=1
if(rad_count>60):
rad_count=60
radius.pop(0)
# Print the radius of curvature
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(out_img,'Radius of Curvature = '+str(np.average(radius))+'(m)',(100,30), font, 1, (255,255,255), 2, cv2.LINE_AA)
# Calculte the offest
offset = ((coord_fit[0,:,1][(image.shape[0]-1)]+coord_fit[0,:,2][(image.shape[0]-1)]-image.shape[1])*(3.7/1400))
font = cv2.FONT_HERSHEY_SIMPLEX
# Print the offset
cv2.putText(out_img,'Vehicle is '+str(offset)+'(m) left of center',(100,80), font, 1, (255,255,255), 2, cv2.LINE_AA)
# Return the overlayed image
return out_img
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from moviepy.editor import *
cap = VideoFileClip('project_video.mp4')
modified_clip = cap.fl_image( process_image )
%time modified_clip.write_videofile("output_videos/project_video.mp4")
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format("output_videos/project_video.mp4"))